Inulin is a soluble dietary fibre, also classified as a prebiotic, extracted from chicory roots. The present study aimed to determine the effect of consumption of native chicory inulin on the stool frequency of middle-aged to older adults (40–75 years old) with uncomfortably but not clinically relevant low stool frequency, specified as two to four days without bowel movements per week. Two randomised, double blind, placebo-controlled crossover trials were conducted using similar protocols in differing populations. Trial A was conducted in Amsterdam, The Netherlands and subsequently Trial B was conducted in Newcastle, United Kingdom. Both trials involved supplementation for 5 weeks with 10 g per day of inulin or placebo, a washout period of 2 weeks, and then crossed over to receive the other treatment. In Trial B, faecal gut microbiota composition was assessed using 16S rRNA gene sequencing. In Trial A, which 10 volunteers completed, the stool frequency was significantly increased to an average 4.9 ± 0.23 (SEM) times per week during inulin periods versus 3.6 ± 0.25 in the periods with placebo (p = 0.01). In contrast, in Trial B which 20 volunteers completed, there was no significant effect of the inulin on stool frequency (7.5 ± 2.1 times per week with inulin, 8.1 ± 3.0 with placebo, p = 0.35). However, many subjects in Trial B had a stool frequency >5 per week also for the placebo period, in breach of the inclusion criteria. Combining the data of 16 low stool frequency subjects from Trials A and B showed a significant effect of inulin to increase stool frequency from 4.1 to 5.0 per week (p = 0.032). Regarding secondary outcomes, stool consistency was significantly softer with inulin treatment compared to placebo periods, it increased 0.29 on the Bristol stool scale (p = 0.008) when data from all subjects of Trials A and B were combined. No other differences in bowel habit parameters due to inulin consumption were significant. None of the differences in specific bacterial abundance, alpha or beta diversity were significant, however the trends were in directions consistent with published studies on other types of inulin. We conclude that 10 g per day of native chicory inulin can increase stool frequency in subjects with low stool frequency.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
This research contributes to understanding and shaping systems for OFMSW separation at urban Small and Medium Enterprises (SMEs, such as offices, shops and service providers). Separating SMEs’ organic fraction of municipal solid waste (OFMSW) is both an opportunity and a serious challenge for the transition towards circular cities. It is an opportunity because OFMSW represents approximately 40% of the total waste mass generated by these companies. It is challenging because post-collection separation is not feasible for OFMSW. Therefore, SMEs disposing of waste should separate their solid waste so that processing the organic fraction for reuse and recycling is practical and attainable. However, these companies do not experience direct advantages from the extra efforts in separating waste, and much of the OFMSW ends up in landfills, often resulting in unnecessary GHG emissions. Therefore, governments and waste processors are looking for ways to improve the OFMSW separation degree by urban companies disposing of waste through policies for behaviour change.There are multiple types of personnel at companies disposing of waste. These co-workers act according to their values, beliefs and norms. They adapt their behaviour continuously, influenced by the physical environment, events over time and self-evaluation of their actions. Therefore, waste separation at companies can be regarded as a Socio-Technical Complex Adaptive System (STCAS). Agent-based modelling and simulation are powerful methods to help understand STCAS. Consequently, we have created an agent-based model representing the evolution of behaviour regarding waste separation at companies in the urban environment. The model aims to show public and private stakeholders involved in solid waste collection, transport and processing to what extent behaviour change policies can shape the system towards desired waste separation degrees.We have co-created the model with participants utilising literature and empirical data from a case study on the transition of the waste collection system of a business park located at a former harbour area in Amsterdam, The Netherlands. First, a conceptual model of the system and the environment was set up through participatory workshops, surveys and interviews with stakeholders, domain experts and relevant actors. Together with our case participants, five policies that affect waste separation behaviour were included in the model. To model the behaviour of each company worker’s values, beliefs and norms during the separation and disposal of OFMSW, we have used the Value-Belief-Norm (VBN) Theory by Stern et al. (1999). We have collected data on waste collection behaviour and separation rates through interviews, workshops and a literature study to operationalise and validate the model.Simulation results show how combinations of behaviour profiles affect waste separation rates. Furthermore, findings show that single waste separation policies are often limitedly capable of changing the behaviour in the system. Rather, a combination of information and communication policies is needed to improve the separation of OFMSW, i.e., dissemination of a newsletter, providing personal feedback to the co-workers disposing of waste, and sharing information on the (improvement of) recycling rates.This study contributes to a better understanding of how policies can support co-workers’ pro-environmental behaviour for organic waste separation rates at SMEs. Thus, it shows policymakers how to stimulate the circular transition by actively engaging co-workers’ waste separation behaviour at SMEs. Future work will extend the model’s purpose by including households and policies supporting separating multiple waste types aimed at various R-strategies proposed by Potting et al. (2016).
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